title: Single-trial EEG-based emotion recognition using kernel Eigen-emotion pattern and adaptive support vector machine.
abstract: Single-trial electroencephalography (EEG)-based emotion recognition enables us to perform fast and direct assessments of human emotional states. However, previous works suggest that a great improvement on the classification accuracy of valence and arousal levels is still needed. To address this, we propose a novel emotional EEG feature extraction method: kernel Eigen-emotion pattern (KEEP). An adaptive SVM is also proposed to deal with the problem of learning from imbalanced emotional EEG data sets. In this study, a set of pictures from IAPS are used for emotion induction. Results based on seven participants show that KEEP gives much better classification results than the widely-used EEG frequency band power features. Also, the adaptive SVM greatly improves classification performance of commonly-adopted SVM classifier. Combined use of KEEP and adaptive SVM can achieve high average valence and arousal classification rates of 73.42% and 73.57%. The highest classification rates for valence and arousal are 80% and 79%, respectively. The results are very promising.
authors.forename: Yi-Hung
authors.initials: YH
authors.lastname: Liu
authors.forename: Chien-Te
authors.initials: CT
authors.lastname: Wu
authors.forename: Yung-Hwa
authors.initials: YH
authors.lastname: Kao
authors.forename: Ya-Ting
authors.initials: YT
authors.lastname: Chen
mesh_terms: D000328:Adult; D001143:Arousal; D004569:Electroencephalography; D004644:Emotions; D006801:Humans; D010775:Photic Stimulation; D012984:Software; D060388:Support Vector Machine; D013647:Task Performance and Analysis; D055815:Young Adult